Fp Data Transforms

Practical Data Transformations

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/fp-data-transforms from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Practical Data Transformations This skill covers the data transformations you do every day: working with arrays, reshaping objects, normalizing API responses, grouping data, and safely accessing nested values. Each section shows the imperative approach first, then the functional equivalent, with honest assessments of when each approach shines.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Table of Contents, 1. Array Operations, 2. Object Transformations, 3. Data Normalization, 4. Grouping and Aggregation, 5. Null-Safe Access.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• You need to transform arrays, objects, grouped data, or nested values in TypeScript.
• The task involves reshaping API responses, null-safe access, aggregation, or normalization.
• You want practical functional patterns for everyday data work instead of low-level loops.
• Simple transformations: .map(), .filter(), .reduce() are perfectly good
• No composition needed: You're doing a one-off transformation
• Team familiarity: Everyone knows native methods

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Summary

The functional approach is better when:

• You need to compose operations
• You want reusable transformations
• You value explicit data flow over implicit state
• Type safety for missing values matters

The imperative approach is acceptable when:

• The transformation is a one-off
• The logic is simple and linear
• Performance is critical and you've measured
• The team is more comfortable with it

Imported: Table of Contents

1. Array Operations
2. Object Transformations
3. Data Normalization
4. Grouping and Aggregation
5. Null-Safe Access
6. Real-World Examples
7. When to Use What

Examples

Example 1: Ask for the upstream workflow directly

Use @fp-data-transforms to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @fp-data-transforms against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @fp-data-transforms for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @fp-data-transforms using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 6. Real-World Examples

Example 1: Transform API Response to UI-Ready Data

// API response
interface ApiOrder {
  order_id: string;
  customer: {
    id: string;
    full_name: string;
  };
  line_items: Array<{
    product_id: string;
    product_name: string;
    qty: number;
    unit_price: number;
  }>;
  order_date: string;
  status: 'pending' | 'processing' | 'shipped' | 'delivered';
}

// What the UI needs
interface OrderSummary {
  id: string;
  customerName: string;
  itemCount: number;
  total: number;
  formattedTotal: string;
  date: string;
  statusLabel: string;
  statusColor: string;
}

// Transformation
const STATUS_CONFIG: Record<string, { label: string; color: string }> = {
  pending: { label: 'Pending', color: 'yellow' },
  processing: { label: 'Processing', color: 'blue' },
  shipped: { label: 'Shipped', color: 'purple' },
  delivered: { label: 'Delivered', color: 'green' },
};

const formatCurrency = (cents: number): string =>
  `$${(cents / 100).toFixed(2)}`;

const formatDate = (iso: string): string =>
  new Date(iso).toLocaleDateString('en-US', {
    month: 'short',
    day: 'numeric',
    year: 'numeric',
  });

const toOrderSummary = (order: ApiOrder): OrderSummary => {
  const total = order.line_items.reduce(
    (sum, item) => sum + item.qty * item.unit_price,
    0
  );

  const status = STATUS_CONFIG[order.status] ?? STATUS_CONFIG.pending;

  return {
    id: order.order_id,
    customerName: order.customer.full_name,
    itemCount: order.line_items.reduce((sum, item) => sum + item.qty, 0),
    total,
    formattedTotal: formatCurrency(total),
    date: formatDate(order.order_date),
    statusLabel: status.label,
    statusColor: status.color,
  };
};

// Transform all orders
const toOrderSummaries = (orders: ApiOrder[]): OrderSummary[] =>
  orders.map(toOrderSummary);

Example 2: Merge User Settings with Defaults

interface AppSettings {
  theme: {
    mode: 'light' | 'dark' | 'system';
    primaryColor: string;
    fontSize: 'small' | 'medium' | 'large';
  };
  notifications: {
    email: boolean;
    push: boolean;
    sms: boolean;
    frequency: 'immediate' | 'daily' | 'weekly';
  };
  privacy: {
    showProfile: boolean;
    showActivity: boolean;
    allowAnalytics: boolean;
  };
}

type DeepPartial<T> = {
  [P in keyof T]?: T[P] extends object ? DeepPartial<T[P]> : T[P];
};

const DEFAULT_SETTINGS: AppSettings = {
  theme: {
    mode: 'system',
    primaryColor: '#007bff',
    fontSize: 'medium',
  },
  notifications: {
    email: true,
    push: true,
    sms: false,
    frequency: 'immediate',
  },
  privacy: {
    showProfile: true,
    showActivity: true,
    allowAnalytics: true,
  },
};

const deepMergeSettings = (
  defaults: AppSettings,
  user: DeepPartial<AppSettings>
): AppSettings => ({
  theme: { ...defaults.theme, ...user.theme },
  notifications: { ...defaults.notifications, ...user.notifications },
  privacy: { ...defaults.privacy, ...user.privacy },
});

// Usage
const userPreferences: DeepPartial<AppSettings> = {
  theme: { mode: 'dark' },
  notifications: { sms: true, frequency: 'daily' },
};

const finalSettings = deepMergeSettings(DEFAULT_SETTINGS, userPreferences);

Example 3: Group Orders by Customer with Totals

interface Order {
  id: string;
  customerId: string;
  customerName: string;
  items: Array<{ name: string; price: number; quantity: number }>;
  date: string;
}

interface CustomerOrderSummary {
  customerId: string;
  customerName: string;
  orderCount: number;
  totalSpent: number;
  orders: Order[];
}

const calculateOrderTotal = (order: Order): number =>
  order.items.reduce((sum, item) => sum + item.price * item.quantity, 0);

const groupOrdersByCustomer = (orders: Order[]): CustomerOrderSummary[] => {
  const grouped = groupBy((order: Order) => order.customerId)(orders);

  return Object.entries(grouped).map(([customerId, customerOrders]) => ({
    customerId,
    customerName: customerOrders[0].customerName,
    orderCount: customerOrders.length,
    totalSpent: customerOrders.reduce(
      (sum, order) => sum + calculateOrderTotal(order),
      0
    ),
    orders: customerOrders,
  }));
};

Example 4: Safely Access Deeply Nested Config

interface AppConfig {
  services?: {
    api?: {
      endpoints?: {
        users?: string;
        orders?: string;
        products?: string;
      };
      auth?: {
        type?: 'bearer' | 'basic' | 'oauth';
        token?: string;
      };
    };
    database?: {
      primary?: {
        host?: string;
        port?: number;
        name?: string;
      };
    };
  };
}

import * as O from 'fp-ts/Option';
import { pipe } from 'fp-ts/function';

// Create a type-safe config accessor
const getConfigValue = <T>(
  config: AppConfig,
  path: (config: AppConfig) => T | undefined,
  defaultValue: T
): T => path(config) ?? defaultValue;

// Usage with optional chaining (simplest)
const apiUsersEndpoint = getConfigValue(
  config,
  c => c.services?.api?.endpoints?.users,
  '/api/users'
);

// For more complex scenarios, use Option
const getEndpoint = (config: AppConfig, name: 'users' | 'orders' | 'products'): string =>
  pipe(
    O.fromNullable(config.services),
    O.flatMap(s => O.fromNullable(s.api)),
    O.flatMap(a => O.fromNullable(a.endpoints)),
    O.flatMap(e => O.fromNullable(e[name])),
    O.getOrElse(() => `/api/${name}`)
  );

// Reusable pattern for multiple values
const getDbConfig = (config: AppConfig) => ({
  host: config.services?.database?.primary?.host ?? 'localhost',
  port: config.services?.database?.primary?.port ?? 5432,
  name: config.services?.database?.primary?.name ?? 'app',
});

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/fp-data-transforms, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 1. Array Operations

Array operations are the bread and butter of data transformation. Let's replace verbose loops with expressive, chainable operations.

Map: Transform Every Element

The Task: Convert an array of prices from cents to dollars.

Imperative Approach

const pricesInCents = [999, 1499, 2999, 4999];

function convertToDollars(prices: number[]): number[] {
  const result: number[] = [];
  for (let i = 0; i < prices.length; i++) {
    result.push(prices[i] / 100);
  }
  return result;
}

const dollars = convertToDollars(pricesInCents);
// [9.99, 14.99, 29.99, 49.99]

Functional Approach

const pricesInCents = [999, 1499, 2999, 4999];

const toDollars = (cents: number): number => cents / 100;

const dollars = pricesInCents.map(toDollars);
// [9.99, 14.99, 29.99, 49.99]

Why functional is better here: The intent is immediately clear. map says "transform each element." The transformation logic (toDollars) is named and reusable. No index management, no manual array building.

Filter: Keep What Matches

The Task: Get all active users from a list.

Imperative Approach

interface User {
  id: string;
  name: string;
  isActive: boolean;
}

function getActiveUsers(users: User[]): User[] {
  const result: User[] = [];
  for (const user of users) {
    if (user.isActive) {
      result.push(user);
    }
  }
  return result;
}

Functional Approach

const isActive = (user: User): boolean => user.isActive;

const activeUsers = users.filter(isActive);

// Or inline for simple predicates
const activeUsers = users.filter(user => user.isActive);

Why functional is better here: The predicate (isActive) is separated from the iteration logic. You can reuse, test, and compose predicates independently.

Reduce: Accumulate Into Something New

The Task: Calculate the total price of items in a cart.

Imperative Approach

interface CartItem {
  name: string;
  price: number;
  quantity: number;
}

function calculateTotal(items: CartItem[]): number {
  let total = 0;
  for (const item of items) {
    total += item.price * item.quantity;
  }
  return total;
}

Functional Approach

const calculateTotal = (items: CartItem[]): number =>
  items.reduce(
    (total, item) => total + item.price * item.quantity,
    0
  );

// Or break out the line total calculation
const lineTotal = (item: CartItem): number => item.price * item.quantity;

const calculateTotal = (items: CartItem[]): number =>
  items.map(lineTotal).reduce((a, b) => a + b, 0);

Honest assessment: For simple sums, the imperative loop is actually quite readable. The functional version shines when you need to compose the accumulation with other transformations, or when the reduction logic is complex enough to benefit from being named.

Chaining: Combine Operations

The Task: Get the names of all active premium users, sorted alphabetically.

Imperative Approach

interface User {
  id: string;
  name: string;
  isActive: boolean;
  tier: 'free' | 'premium';
}

function getActivePremiumNames(users: User[]): string[] {
  const result: string[] = [];
  for (const user of users) {
    if (user.isActive && user.tier === 'premium') {
      result.push(user.name);
    }
  }
  result.sort((a, b) => a.localeCompare(b));
  return result;
}

Functional Approach

const getActivePremiumNames = (users: User[]): string[] =>
  users
    .filter(user => user.isActive)
    .filter(user => user.tier === 'premium')
    .map(user => user.name)
    .sort((a, b) => a.localeCompare(b));

// Or with named predicates for reuse
const isActive = (user: User): boolean => user.isActive;
const isPremium = (user: User): boolean => user.tier === 'premium';
const getName = (user: User): string => user.name;
const alphabetically = (a: string, b: string): number => a.localeCompare(b);

const getActivePremiumNames = (users: User[]): string[] =>
  users
    .filter(isActive)
    .filter(isPremium)
    .map(getName)
    .sort(alphabetically);

Why functional is better here: Each step in the chain has a single responsibility. You can read the transformation as a series of steps: "filter active, filter premium, get names, sort." Adding or removing a step is trivial.

Using fp-ts Array Module

fp-ts provides additional array utilities with better composition support:

import * as A from 'fp-ts/Array';
import * as O from 'fp-ts/Option';
import { pipe } from 'fp-ts/function';

// Safe head (first element)
const first = pipe(
  [1, 2, 3],
  A.head
); // Some(1)

const firstOfEmpty = pipe(
  [] as number[],
  A.head
); // None

// Safe lookup by index
const third = pipe(
  ['a', 'b', 'c', 'd'],
  A.lookup(2)
); // Some('c')

// Find with predicate
const found = pipe(
  users,
  A.findFirst(user => user.id === 'abc123')
); // Option<User>

// Partition into two groups
const [inactive, active] = pipe(
  users,
  A.partition(user => user.isActive)
);

// Take first N elements
const topThree = pipe(
  sortedScores,
  A.takeLeft(3)
);

// Unique values
const uniqueTags = pipe(
  allTags,
  A.uniq({ equals: (a, b) => a === b })
);

Imported: 2. Object Transformations

Objects need reshaping constantly: picking fields, omitting sensitive data, merging settings, and updating nested values.

Pick: Select Specific Fields

The Task: Extract only the public fields from a user object.

Imperative Approach

interface User {
  id: string;
  name: string;
  email: string;
  passwordHash: string;
  internalNotes: string;
}

function getPublicUser(user: User): { id: string; name: string; email: string } {
  return {
    id: user.id,
    name: user.name,
    email: user.email,
  };
}

Functional Approach

// Generic pick utility
const pick = <T extends object, K extends keyof T>(
  keys: K[]
) => (obj: T): Pick<T, K> =>
  keys.reduce(
    (result, key) => {
      result[key] = obj[key];
      return result;
    },
    {} as Pick<T, K>
  );

const getPublicUser = pick<User, 'id' | 'name' | 'email'>(['id', 'name', 'email']);

const publicUser = getPublicUser(user);

Why functional is better here: The pick utility is reusable across your codebase. Type safety ensures you can only pick keys that exist.

Omit: Remove Specific Fields

The Task: Remove sensitive fields before logging.

Imperative Approach

function sanitizeForLogging(user: User): Omit<User, 'passwordHash' | 'internalNotes'> {
  const { passwordHash, internalNotes, ...safe } = user;
  return safe;
}

Functional Approach

// Generic omit utility
const omit = <T extends object, K extends keyof T>(
  keys: K[]
) => (obj: T): Omit<T, K> => {
  const result = { ...obj };
  for (const key of keys) {
    delete result[key];
  }
  return result as Omit<T, K>;
};

const sanitizeForLogging = omit<User, 'passwordHash' | 'internalNotes'>([
  'passwordHash',
  'internalNotes',
]);

Honest assessment: For one-off omits, destructuring (the imperative approach) is perfectly fine and very readable. The functional omit utility pays off when you have many such transformations or need to compose them.

Merge: Combine Objects

The Task: Merge user settings with defaults.

Imperative Approach

interface Settings {
  theme: 'light' | 'dark';
  fontSize: number;
  notifications: boolean;
  language: string;
}

function mergeSettings(
  defaults: Settings,
  userSettings: Partial<Settings>
): Settings {
  return {
    theme: userSettings.theme !== undefined ? userSettings.theme : defaults.theme,
    fontSize: userSettings.fontSize !== undefined ? userSettings.fontSize : defaults.fontSize,
    notifications: userSettings.notifications !== undefined
      ? userSettings.notifications
      : defaults.notifications,
    language: userSettings.language !== undefined ? userSettings.language : defaults.language,
  };
}

Functional Approach

const mergeSettings = (
  defaults: Settings,
  userSettings: Partial<Settings>
): Settings => ({
  ...defaults,
  ...userSettings,
});

// Usage
const defaults: Settings = {
  theme: 'light',
  fontSize: 14,
  notifications: true,
  language: 'en',
};

const userPrefs: Partial<Settings> = {
  theme: 'dark',
  fontSize: 16,
};

const finalSettings = mergeSettings(defaults, userPrefs);
// { theme: 'dark', fontSize: 16, notifications: true, language: 'en' }

Why functional is better here: Spread syntax is concise and handles any number of keys. Later spreads override earlier ones, giving you natural "defaults with overrides" behavior.

Deep Merge: Nested Object Combination

The Task: Merge nested configuration objects.

Imperative Approach

interface Config {
  api: {
    baseUrl: string;
    timeout: number;
    retries: number;
  };
  ui: {
    theme: string;
    animations: boolean;
  };
}

function deepMerge(
  target: Config,
  source: Partial<Config>
): Config {
  const result = { ...target };

  if (source.api) {
    result.api = { ...target.api, ...source.api };
  }
  if (source.ui) {
    result.ui = { ...target.ui, ...source.ui };
  }

  return result;
}

Functional Approach

// Generic deep merge for one level of nesting
const deepMerge = <T extends Record<string, object>>(
  target: T,
  source: { [K in keyof T]?: Partial<T[K]> }
): T => {
  const result = { ...target };

  for (const key of Object.keys(source) as Array<keyof T>) {
    if (source[key] !== undefined) {
      result[key] = { ...target[key], ...source[key] };
    }
  }

  return result;
};

// Usage
const defaultConfig: Config = {
  api: { baseUrl: 'https://api.example.com', timeout: 5000, retries: 3 },
  ui: { theme: 'light', animations: true },
};

const customConfig = deepMerge(defaultConfig, {
  api: { timeout: 10000 },
  ui: { theme: 'dark' },
});
// api.baseUrl preserved, api.timeout overridden
// ui.theme overridden, ui.animations preserved

Immutable Updates: Change Nested Values

The Task: Update a deeply nested value without mutation.

Imperative (Mutating) Approach

interface State {
  user: {
    profile: {
      settings: {
        theme: string;
      };
    };
  };
}

function updateTheme(state: State, newTheme: string): void {
  state.user.profile.settings.theme = newTheme; // Mutation!
}

Functional (Immutable) Approach

// Manual spread nesting
const updateTheme = (state: State, newTheme: string): State => ({
  ...state,
  user: {
    ...state.user,
    profile: {
      ...state.user.profile,
      settings: {
        ...state.user.profile.settings,
        theme: newTheme,
      },
    },
  },
});

// With a lens-like helper
const updatePath = <T, V>(
  obj: T,
  path: string[],
  value: V
): T => {
  if (path.length === 0) return value as unknown as T;

  const [head, ...rest] = path;
  return {
    ...obj,
    [head]: updatePath((obj as Record<string, unknown>)[head], rest, value),
  } as T;
};

const newState = updatePath(state, ['user', 'profile', 'settings', 'theme'], 'dark');

Honest assessment: The spread nesting is verbose but explicit. For deeply nested updates, consider using a library like immer or fp-ts lenses. The verbosity of the functional approach is the price of immutability.

Imported: 3. Data Normalization

API responses rarely match the shape your app needs. Normalization transforms nested, denormalized data into flat, indexed structures.

API Response to App State

The Task: Transform a nested API response into a normalized state.

API Response (What You Get)

interface ApiResponse {
  orders: Array<{
    id: string;
    customerId: string;
    customerName: string;
    customerEmail: string;
    items: Array<{
      productId: string;
      productName: string;
      quantity: number;
      price: number;
    }>;
    total: number;
    status: string;
  }>;
}

App State (What You Need)

interface NormalizedState {
  orders: {
    byId: Record<string, Order>;
    allIds: string[];
  };
  customers: {
    byId: Record<string, Customer>;
    allIds: string[];
  };
  products: {
    byId: Record<string, Product>;
    allIds: string[];
  };
}

interface Order {
  id: string;
  customerId: string;
  itemIds: string[];
  total: number;
  status: string;
}

interface Customer {
  id: string;
  name: string;
  email: string;
}

interface Product {
  id: string;
  name: string;
  price: number;
}

Imperative Approach

function normalizeApiResponse(response: ApiResponse): NormalizedState {
  const state: NormalizedState = {
    orders: { byId: {}, allIds: [] },
    customers: { byId: {}, allIds: [] },
    products: { byId: {}, allIds: [] },
  };

  for (const order of response.orders) {
    // Extract customer
    if (!state.customers.byId[order.customerId]) {
      state.customers.byId[order.customerId] = {
        id: order.customerId,
        name: order.customerName,
        email: order.customerEmail,
      };
      state.customers.allIds.push(order.customerId);
    }

    // Extract products and build item IDs
    const itemIds: string[] = [];
    for (const item of order.items) {
      if (!state.products.byId[item.productId]) {
        state.products.byId[item.productId] = {
          id: item.productId,
          name: item.productName,
          price: item.price,
        };
        state.products.allIds.push(item.productId);
      }
      itemIds.push(item.productId);
    }

    // Add normalized order
    state.orders.byId[order.id] = {
      id: order.id,
      customerId: order.customerId,
      itemIds,
      total: order.total,
      status: order.status,
    };
    state.orders.allIds.push(order.id);
  }

  return state;
}

Functional Approach

import { pipe } from 'fp-ts/function';
import * as A from 'fp-ts/Array';
import * as R from 'fp-ts/Record';

// Helper to create normalized collection
interface NormalizedCollection<T extends { id: string }> {
  byId: Record<string, T>;
  allIds: string[];
}

const createNormalizedCollection = <T extends { id: string }>(
  items: T[]
): NormalizedCollection<T> => ({
  byId: pipe(
    items,
    A.reduce({} as Record<string, T>, (acc, item) => ({
      ...acc,
      [item.id]: item,
    }))
  ),
  allIds: items.map(item => item.id),
});

// Extract entities
const extractCustomers = (orders: ApiResponse['orders']): Customer[] =>
  pipe(
    orders,
    A.map(order => ({
      id: order.customerId,
      name: order.customerName,
      email: order.customerEmail,
    })),
    A.uniq({ equals: (a, b) => a.id === b.id })
  );

const extractProducts = (orders: ApiResponse['orders']): Product[] =>
  pipe(
    orders,
    A.flatMap(order => order.items),
    A.map(item => ({
      id: item.productId,
      name: item.productName,
      price: item.price,
    })),
    A.uniq({ equals: (a, b) => a.id === b.id })
  );

const extractOrders = (orders: ApiResponse['orders']): Order[] =>
  orders.map(order => ({
    id: order.id,
    customerId: order.customerId,
    itemIds: order.items.map(item => item.productId),
    total: order.total,
    status: order.status,
  }));

// Compose into final normalization
const normalizeApiResponse = (response: ApiResponse): NormalizedState => ({
  orders: createNormalizedCollection(extractOrders(response.orders)),
  customers: createNormalizedCollection(extractCustomers(response.orders)),
  products: createNormalizedCollection(extractProducts(response.orders)),
});

Why functional is better here: Each extraction is independent and testable. The createNormalizedCollection helper is reusable. Adding a new entity type means adding one new extraction function.

Transform API Response to UI-Ready Data

The Task: Convert API data to what your components need.

// API gives you this
interface ApiUser {
  user_id: string;
  first_name: string;
  last_name: string;
  email_address: string;
  created_at: string; // ISO string
  avatar_url: string | null;
}

// Components need this
interface DisplayUser {
  id: string;
  fullName: string;
  email: string;
  memberSince: string; // "Jan 2024"
  avatarUrl: string; // With fallback
}

Functional Approach

const formatDate = (isoString: string): string => {
  const date = new Date(isoString);
  return date.toLocaleDateString('en-US', { month: 'short', year: 'numeric' });
};

const DEFAULT_AVATAR = 'https://example.com/default-avatar.png';

const toDisplayUser = (apiUser: ApiUser): DisplayUser => ({
  id: apiUser.user_id,
  fullName: `${apiUser.first_name} ${apiUser.last_name}`,
  email: apiUser.email_address,
  memberSince: formatDate(apiUser.created_at),
  avatarUrl: apiUser.avatar_url ?? DEFAULT_AVATAR,
});

// Transform array of users
const toDisplayUsers = (apiUsers: ApiUser[]): DisplayUser[] =>
  apiUsers.map(toDisplayUser);

Imported: 4. Grouping and Aggregation

Grouping and aggregating data is essential for reports, dashboards, and analytics.

GroupBy: Organize by Key

The Task: Group orders by customer.

Imperative Approach

interface Order {
  id: string;
  customerId: string;
  total: number;
  date: string;
}

function groupByCustomer(orders: Order[]): Record<string, Order[]> {
  const result: Record<string, Order[]> = {};

  for (const order of orders) {
    if (!result[order.customerId]) {
      result[order.customerId] = [];
    }
    result[order.customerId].push(order);
  }

  return result;
}

Functional Approach

// Generic groupBy utility
const groupBy = <T, K extends string | number>(
  getKey: (item: T) => K
) => (items: T[]): Record<K, T[]> =>
  items.reduce(
    (groups, item) => {
      const key = getKey(item);
      return {
        ...groups,
        [key]: [...(groups[key] || []), item],
      };
    },
    {} as Record<K, T[]>
  );

// Usage
const groupByCustomer = groupBy<Order, string>(order => order.customerId);
const ordersByCustomer = groupByCustomer(orders);

// Or inline
const ordersByStatus = groupBy((order: Order) => order.status)(orders);

Using fp-ts NonEmptyArray.groupBy:

import * as NEA from 'fp-ts/NonEmptyArray';
import { pipe } from 'fp-ts/function';

// NEA.groupBy guarantees non-empty arrays in result
const ordersByCustomer = pipe(
  orders as NEA.NonEmptyArray<Order>, // Must be non-empty
  NEA.groupBy(order => order.customerId)
); // Record<string, NonEmptyArray<Order>>

CountBy: Count Occurrences

The Task: Count orders by status.

Imperative Approach

function countByStatus(orders: Order[]): Record<string, number> {
  const counts: Record<string, number> = {};

  for (const order of orders) {
    counts[order.status] = (counts[order.status] || 0) + 1;
  }

  return counts;
}

Functional Approach

// Generic countBy utility
const countBy = <T, K extends string>(
  getKey: (item: T) => K
) => (items: T[]): Record<K, number> =>
  items.reduce(
    (counts, item) => {
      const key = getKey(item);
      return {
        ...counts,
        [key]: (counts[key] || 0) + 1,
      };
    },
    {} as Record<K, number>
  );

// Usage
const orderCountByStatus = countBy((order: Order) => order.status)(orders);
// { pending: 5, shipped: 12, delivered: 8 }

SumBy: Aggregate Numeric Values

The Task: Calculate total revenue per product category.

Imperative Approach

interface Sale {
  productId: string;
  category: string;
  amount: number;
}

function sumByCategory(sales: Sale[]): Record<string, number> {
  const totals: Record<string, number> = {};

  for (const sale of sales) {
    totals[sale.category] = (totals[sale.category] || 0) + sale.amount;
  }

  return totals;
}

Functional Approach

// Generic sumBy utility
const sumBy = <T, K extends string>(
  getKey: (item: T) => K,
  getValue: (item: T) => number
) => (items: T[]): Record<K, number> =>
  items.reduce(
    (totals, item) => {
      const key = getKey(item);
      return {
        ...totals,
        [key]: (totals[key] || 0) + getValue(item),
      };
    },
    {} as Record<K, number>
  );

// Usage
const revenueByCategory = sumBy(
  (sale: Sale) => sale.category,
  (sale: Sale) => sale.amount
)(sales);
// { electronics: 15000, clothing: 8500, books: 3200 }

Complex Aggregation Example

The Task: Calculate totals from line items with quantity and unit price.

interface LineItem {
  productId: string;
  productName: string;
  quantity: number;
  unitPrice: number;
}

interface Invoice {
  id: string;
  lineItems: LineItem[];
  taxRate: number;
}

Functional Approach

const lineTotal = (item: LineItem): number =>
  item.quantity * item.unitPrice;

const subtotal = (items: LineItem[]): number =>
  items.reduce((sum, item) => sum + lineTotal(item), 0);

const calculateTax = (amount: number, rate: number): number =>
  amount * rate;

const calculateInvoiceTotal = (invoice: Invoice): {
  subtotal: number;
  tax: number;
  total: number;
} => {
  const sub = subtotal(invoice.lineItems);
  const tax = calculateTax(sub, invoice.taxRate);

  return {
    subtotal: sub,
    tax,
    total: sub + tax,
  };
};

// With fp-ts pipe for clarity
import { pipe } from 'fp-ts/function';

const calculateInvoiceTotal = (invoice: Invoice) => {
  const sub = pipe(
    invoice.lineItems,
    A.map(lineTotal),
    A.reduce(0, (a, b) => a + b)
  );

  return {
    subtotal: sub,
    tax: sub * invoice.taxRate,
    total: sub * (1 + invoice.taxRate),
  };
};

Imported: 5. Null-Safe Access

Stop writing if (x && x.y && x.y.z). Safely navigate nested structures without runtime errors.

The Problem

interface Config {
  database?: {
    connection?: {
      host?: string;
      port?: number;
    };
    pool?: {
      max?: number;
    };
  };
  features?: {
    experimental?: {
      enabled?: boolean;
    };
  };
}

Imperative (Verbose) Approach

function getDatabaseHost(config: Config): string {
  if (
    config.database &&
    config.database.connection &&
    config.database.connection.host
  ) {
    return config.database.connection.host;
  }
  return 'localhost';
}

Optional Chaining (Modern TypeScript)

const getDatabaseHost = (config: Config): string =>
  config.database?.connection?.host ?? 'localhost';

Honest assessment: For simple access patterns, optional chaining (?.) is perfect. It's built into the language and very readable. Use fp-ts Option when you need to compose operations on potentially missing values.

When to Use Option Instead

Use fp-ts Option when:

• You need to chain multiple operations on potentially missing values
• You want to distinguish "missing" from other falsy values
• You're building a pipeline of transformations

import * as O from 'fp-ts/Option';
import { pipe } from 'fp-ts/function';

// Safe property access that returns Option
const prop = <T, K extends keyof T>(key: K) =>
  (obj: T | null | undefined): O.Option<T[K]> =>
    obj != null && key in obj
      ? O.some(obj[key] as T[K])
      : O.none;

// Chain accesses with flatMap
const getDatabaseHost = (config: Config): O.Option<string> =>
  pipe(
    O.some(config),
    O.flatMap(prop('database')),
    O.flatMap(prop('connection')),
    O.flatMap(prop('host'))
  );

// Extract with default
const host = pipe(
  getDatabaseHost(config),
  O.getOrElse(() => 'localhost')
);

Safe Array Access

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